Data-Driven Decision Making and the Future of Big Data

In this video, Yael Garten explores data-driven decision making and the future of big data transformation in business.

Read the transcript:One way to define analytics is using data to measure, understand and inform and improve our products, our business strategy. How do we use data and convert it into something that leads to actionable insights and leads to better, more impactful decision making, faster decision making, more accurate decision making, and so whether that's in changing or improving our products, in informing what we build and informing what we invest in and informing what we roll out and what we roll back in the online consumer Web world. That's what it means to me. That's how we use it at LinkedIn.

One thing is that is important is to actually build good data foundations, to enable analytics or data scientists to actually make use of this data in the right and in involving ways.

In the consumer space, what we do is a lot of online experimentation. You don't roll out a change before actually first measuring what is the impact it has on users, on members, on sort of downstream engagement with the site, and so every single change that's rolled out is usually very meticulously analyzed.

People often ask me, "When is the right time to start thinking about data?" or, "When is the right time to start using data in my company or thinking about hiring an analyst or a scientist or data scientist?" So it's never too early to start thinking about it, because it might inform what you're collecting, it might inform what decisions you're making, how you're setting up, you know, your company or your organization. So I'd say it's really never too early.

For people getting into analytics I think that solicitation of additional context to understand how to make their work most impactful is really important.

They have a voice in helping to determine things in the organization or in the company in terms of what data is actually at their disposal and at their dispense, and how can they change the quality of that data, and how can they change the tools that's to access that data and to help others access that data?

There’s so much data around this. The world is full of data. Like the natural world emits data. We are now starting to instrument products and companies that create data, that log data, that emit data. There is such interesting ways to mash up data. For all of these different applications, how could we not?

So that's what I see as the future of analytics, basically using data really to either determine and drive, or at least inform every single decision that's made and every single product that's built. So that's where my passion comes from.